blob: 54f25968910a9f2325659a1608e849b1c5721100 [file] [log] [blame]
// Copyright (c) Facebook, Inc. and its affiliates.
// All rights reserved.
//
// Copyright 2019 Google LLC
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.
#include <assert.h>
#include <math.h>
#include <stdbool.h>
#include <stddef.h>
#include <stdint.h>
#include <stdlib.h>
#include <string.h>
#include <xnnpack.h>
#include <xnnpack/allocator.h>
#include <xnnpack/operator.h>
#include <xnnpack/common.h>
#include <xnnpack/log.h>
#include <xnnpack/math.h>
#include <xnnpack/params-init.h>
#include <xnnpack/params.h>
#include <xnnpack/indirection.h>
static inline size_t compute_output_dimension(
size_t padded_input_dimension,
size_t pooling_dimension,
size_t stride_dimension)
{
return (padded_input_dimension - pooling_dimension) / stride_dimension + 1;
}
enum xnn_status xnn_create_average_pooling2d_nhwc_q8(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t pooling_height,
uint32_t pooling_width,
uint32_t stride_height,
uint32_t stride_width,
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
uint8_t input_zero_point,
float input_scale,
uint8_t output_zero_point,
float output_scale,
uint8_t output_min,
uint8_t output_max,
uint32_t flags,
xnn_operator_t* average_pooling_op_out)
{
xnn_operator_t average_pooling_op = NULL;
enum xnn_status status = xnn_status_uninitialized;
if (!xnn_params.initialized) {
xnn_log_error("failed to create Average Pooling operator: XNNPACK is not initialized");
goto error;
}
status = xnn_status_invalid_parameter;
const uint32_t pooling_size = pooling_height * pooling_width;
if (pooling_size == 0) {
xnn_log_error(
"failed to create Average Pooling operator with %" PRIu32 "x%" PRIu32 " pooling size: "
"pooling size dimensions must be non-zero",
pooling_width, pooling_height);
goto error;
}
if (pooling_size == 1) {
xnn_log_error(
"failed to create Average Pooling operator with 1 pooling element: 1x1 pooling is meaningless");
goto error;
}
if (stride_height == 0 || stride_width == 0) {
xnn_log_error(
"failed to create Average Pooling operator with %" PRIu32 "x%" PRIu32 " stride: "
"stride dimensions must be non-zero",
stride_width, stride_height);
goto error;
}
if (channels == 0) {
xnn_log_error(
"failed to create Average Pooling operator with %zu channels: number of channels must be non-zero",
channels);
goto error;
}
if (input_pixel_stride < channels) {
xnn_log_error(
"failed to create Average Pooling operator with input pixel stride of %zu: "
"stride must be at least as large as the number of channels (%zu)",
input_pixel_stride, channels);
goto error;
}
if (output_pixel_stride < channels) {
xnn_log_error(
"failed to create Average Pooling operator with output pixel stride of %zu: "
"stride must be at least as large as the number of channels (%zu)",
output_pixel_stride, channels);
goto error;
}
if (input_scale <= 0.0f || !isnormal(input_scale)) {
xnn_log_error(
"failed to create Average Pooling operator with %.7g input scale: "
"scale must be finite, normalized, and positive",
input_scale);
goto error;
}
if (output_scale <= 0.0f || !isnormal(output_scale)) {
xnn_log_error(
"failed to create Average Pooling operator with %.7g output scale: "
"scale must be finite, normalized, and positive",
output_scale);
goto error;
}
if (output_min >= output_max) {
xnn_log_error(
"failed to create Average Pooling operator with [%" PRIu8 ", %" PRIu8 "] output range: "
"range min must be below range max",
output_min, output_max);
goto error;
}
status = xnn_status_unsupported_parameter;
const float input_output_scale = input_scale / output_scale;
if (input_output_scale < 0x1.0p-8f || input_output_scale >= 0x1.0p+8f) {
xnn_log_error(
"failed to create Average Pooling operator with %.7g input scale and %.7g output scale: "
"input-to-output scale ratio (%.7f) must be in [2**-8, 2**8) range",
input_scale, output_scale, input_output_scale);
goto error;
}
if (pooling_size >= 16777216) {
xnn_log_error(
"failed to create Average Pooling operator with %"PRIu32" (%" PRIu32 "x%" PRIu32 ") pooling elements: "
"the number of elements in the pooling area must be below 2**24",
pooling_size, pooling_width, pooling_height);
goto error;
}
status = xnn_status_out_of_memory;
average_pooling_op = xnn_allocate_zero_memory(sizeof(struct xnn_operator));
if (average_pooling_op == NULL) {
xnn_log_error("failed to allocate %zu bytes for Average Pooling operator descriptor", sizeof(struct xnn_operator));
goto error;
}
const bool any_padding = (input_padding_left | input_padding_top | input_padding_right | input_padding_bottom) != 0;
const uint32_t mr = xnn_params.q8.avgpool.mr;
const uint32_t qr = xnn_params.q8.avgpool.qr;
if (any_padding || pooling_size < mr || (pooling_size - mr) % qr != 0) {
void* zero_buffer = xnn_allocate_memory(channels * sizeof(uint8_t) + XNN_EXTRA_BYTES);
if (zero_buffer == NULL) {
xnn_log_error("failed to allocate %zu bytes for Average Pooling zero padding",
channels * sizeof(uint8_t) + XNN_EXTRA_BYTES);
goto error;
}
memset(zero_buffer, input_zero_point, channels * sizeof(uint8_t));
average_pooling_op->zero_buffer = zero_buffer;
}
average_pooling_op->padding_top = input_padding_top;
average_pooling_op->padding_right = input_padding_right;
average_pooling_op->padding_bottom = input_padding_bottom;
average_pooling_op->padding_left = input_padding_left;
average_pooling_op->kernel_height = pooling_height;
average_pooling_op->kernel_width = pooling_width;
average_pooling_op->stride_height = stride_height;
average_pooling_op->stride_width = stride_width;
average_pooling_op->dilation_height = 1;
average_pooling_op->dilation_width = 1;
average_pooling_op->channels = channels;
average_pooling_op->input_pixel_stride = input_pixel_stride;
average_pooling_op->output_pixel_stride = output_pixel_stride;
// Number of rows read in the micro-kernel.
const size_t nrows = round_up(doz(pooling_size, mr), qr) + mr;
average_pooling_op->q8_avgpool_params =
xnn_init_q8_avgpool_params(
(int32_t) -((uint32_t) input_zero_point * (uint32_t) nrows),
input_scale / (output_scale * (float) pooling_size),
output_zero_point, output_min, output_max);
average_pooling_op->type = xnn_operator_type_average_pooling_q8;
average_pooling_op->ukernel.type = xnn_ukernel_type_average_pooling;
*average_pooling_op_out = average_pooling_op;
return xnn_status_success;
error:
xnn_delete_operator(average_pooling_op);
return status;
}
enum xnn_status xnn_create_average_pooling2d_nhwc_f32(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t pooling_height,
uint32_t pooling_width,
uint32_t stride_height,
uint32_t stride_width,
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* average_pooling_op_out)
{
xnn_operator_t average_pooling_op = NULL;
enum xnn_status status = xnn_status_uninitialized;
if (!xnn_params.initialized) {
xnn_log_error("failed to create Average Pooling operator: XNNPACK is not initialized");
goto error;
}
status = xnn_status_invalid_parameter;
const uint32_t pooling_size = pooling_height * pooling_width;
if (pooling_size == 0) {
xnn_log_error(
"failed to create Average Pooling operator with %" PRIu32 "x%" PRIu32 " pooling size: "
"pooling size dimensions must be non-zero",
pooling_width, pooling_height);
goto error;
}
if (pooling_size == 1) {
xnn_log_error(
"failed to create Average Pooling operator with 1 pooling element: 1x1 pooling is meaningless");
goto error;
}
if (stride_height == 0 || stride_width == 0) {
xnn_log_error(
"failed to create Average Pooling operator with %" PRIu32 "x%" PRIu32 " stride: "
"stride dimensions must be non-zero",
stride_width, stride_height);
goto error;
}
if (channels == 0) {
xnn_log_error(
"failed to create Average Pooling operator with %zu channels: number of channels must be non-zero",
channels);
goto error;
}
if (input_pixel_stride < channels) {
xnn_log_error(
"failed to create Average Pooling operator with input pixel stride of %zu: "
"stride must be at least as large as the number of channels (%zu)",
input_pixel_stride, channels);
goto error;
}
if (output_pixel_stride < channels) {
xnn_log_error(
"failed to create Average Pooling operator with output pixel stride of %zu: "
"stride must be at least as large as the number of channels (%zu)",
output_pixel_stride, channels);
goto error;
}
if (isnan(output_min)) {
xnn_log_error(
"failed to create Average Pooling operator with NaN output lower bound: lower bound must be non-NaN");
goto error;
}
if (isnan(output_max)) {
xnn_log_error(
"failed to create Average Pooling operator with NaN output upper bound: upper bound must be non-NaN");
goto error;
}
if (output_min >= output_max) {
xnn_log_error(
"failed to create Average Pooling operator with [%.7g, %.7g] output range: lower bound must be below upper bound",
output_min, output_max);
goto error;
}
status = xnn_status_out_of_memory;
average_pooling_op = xnn_allocate_zero_memory(sizeof(struct xnn_operator));
if (average_pooling_op == NULL) {
xnn_log_error("failed to allocate %zu bytes for Average Pooling operator descriptor", sizeof(struct xnn_operator));
goto error;
}
const bool any_padding = (input_padding_left | input_padding_top | input_padding_right | input_padding_bottom) != 0;
const uint32_t mr = xnn_params.f32.avgpool.mr;
const uint32_t qr = xnn_params.f32.avgpool.qr;
if (any_padding || pooling_size < mr || (pooling_size - mr) % qr != 0) {
void* zero_buffer = xnn_allocate_memory(channels * sizeof(float) + XNN_EXTRA_BYTES);
if (zero_buffer == NULL) {
xnn_log_error("failed to allocate %zu bytes for Average Pooling zero padding",
channels * sizeof(float) + XNN_EXTRA_BYTES);
goto error;
}
memset(zero_buffer, 0, channels * sizeof(float));
average_pooling_op->zero_buffer = zero_buffer;
}
average_pooling_op->padding_top = input_padding_top;
average_pooling_op->padding_right = input_padding_right;
average_pooling_op->padding_bottom = input_padding_bottom;
average_pooling_op->padding_left = input_padding_left;
average_pooling_op->kernel_height = pooling_height;
average_pooling_op->kernel_width = pooling_width;
average_pooling_op->stride_height = stride_height;
average_pooling_op->stride_width = stride_width;
average_pooling_op->dilation_height = 1;
average_pooling_op->dilation_width = 1;
average_pooling_op->channels = channels;
average_pooling_op->input_pixel_stride = input_pixel_stride;
average_pooling_op->output_pixel_stride = output_pixel_stride;
average_pooling_op->type = xnn_operator_type_average_pooling_f32;
if (any_padding) {
average_pooling_op->f32_output_params =
xnn_init_f32_output_params(output_min, output_max);
average_pooling_op->ukernel.type = xnn_ukernel_type_pixelwise_average_pooling;
} else {
average_pooling_op->f32_avgpool_params =
xnn_init_f32_avgpool_params(1.0f / (float) pooling_size, output_min, output_max);
average_pooling_op->ukernel.type = xnn_ukernel_type_average_pooling;
}
*average_pooling_op_out = average_pooling_op;
return xnn_status_success;
error:
xnn_delete_operator(average_pooling_op);
return status;
}
enum xnn_status xnn_setup_average_pooling2d_nhwc_q8(
xnn_operator_t average_pooling_op,
size_t batch_size,
size_t input_height,
size_t input_width,
const uint8_t* input,
uint8_t* output,
pthreadpool_t threadpool)
{
if (average_pooling_op->type != xnn_operator_type_average_pooling_q8) {
xnn_log_error("failed to setup Average Pooling (Q8) operator: operator type mismatch");
return xnn_status_invalid_parameter;
}
average_pooling_op->state = xnn_run_state_invalid;
if (!xnn_params.initialized) {
xnn_log_error("failed to setup Average Pooling operator: XNNPACK is not initialized");
return xnn_status_uninitialized;
}
if (input_width == 0 || input_height == 0) {
xnn_log_error(
"failed to setup Average Pooling operator with %zux%zu input: input dimensions must be non-zero",
input_width, input_height);
return xnn_status_invalid_parameter;
}
if (batch_size == 0) {
average_pooling_op->state = xnn_run_state_skip;
return xnn_status_success;
}
average_pooling_op->batch_size = batch_size;
average_pooling_op->input_height = input_height;
average_pooling_op->input_width = input_width;
average_pooling_op->input = input;
average_pooling_op->output_height = compute_output_dimension(
average_pooling_op->padding_top + input_height + average_pooling_op->padding_bottom,
average_pooling_op->kernel_height,
average_pooling_op->stride_height);
average_pooling_op->output_width = compute_output_dimension(
average_pooling_op->padding_left + input_width + average_pooling_op->padding_right,
average_pooling_op->kernel_width,
average_pooling_op->stride_width);
average_pooling_op->output = output;
size_t valid_batch_size = 0;
if (input == average_pooling_op->last_input &&
input_height == average_pooling_op->last_input_height &&
input_width == average_pooling_op->last_input_width)
{
valid_batch_size = average_pooling_op->valid_batch_size;
if (batch_size <= valid_batch_size) {
average_pooling_op->compute.range[0] = batch_size;
average_pooling_op->state = xnn_run_state_ready;
return xnn_status_success;
}
}
const size_t pooling_height = average_pooling_op->kernel_height;
const size_t pooling_width = average_pooling_op->kernel_width;
const size_t pooling_size = pooling_height * pooling_width;
const size_t output_height = average_pooling_op->output_height;
const size_t output_width = average_pooling_op->output_width;
// Micro-kernel may read up to (mr - 1) elements after the end of indirection buffer.
const uint32_t mr = xnn_params.q8.avgpool.mr;
const size_t step_width = min(average_pooling_op->stride_width, pooling_width);
const size_t step_height = pooling_size + (output_width * step_width - 1) * pooling_height;
const size_t indirection_buffer_size = sizeof(void*) * ((mr - 1) + batch_size * output_height * step_height);
const void** indirection_buffer = (const void**) realloc(average_pooling_op->indirection_buffer, indirection_buffer_size);
if (indirection_buffer == NULL) {
xnn_log_error("failed to allocate %zu bytes for indirection buffer", indirection_buffer_size);
return xnn_status_out_of_memory;
}
average_pooling_op->indirection_buffer = indirection_buffer;
xnn_indirection_init_dwconv2d(
average_pooling_op, valid_batch_size, step_height, step_width, 0 /* log2(sizeof(uint8_t)) */);
const uint32_t qr = xnn_params.q8.avgpool.qr;
const size_t channels = average_pooling_op->channels;
const size_t indirect_input_height_stride = step_height * sizeof(void*);
const size_t output_width_stride = average_pooling_op->output_pixel_stride * sizeof(uint8_t);
const size_t output_height_stride = output_width * output_width_stride;
const size_t multipass_adjustment =
pooling_size > mr ? round_up(pooling_size - mr, qr) + mr - qr : 0;
average_pooling_op->context.average_pooling = (struct average_pooling_context) {
.indirect_input = indirection_buffer,
.indirect_input_batch_stride = output_height * indirect_input_height_stride,
.indirect_input_height_stride = indirect_input_height_stride,
.output = output,
.output_batch_stride = output_height * output_height_stride,
.output_height_stride = output_height_stride,
.output_width = output_width,
.pooling_size = pooling_size,
.channels = channels,
.zero = average_pooling_op->zero_buffer,
.input_increment = (pooling_height * step_width - multipass_adjustment) * sizeof(void*),
.output_increment = output_width_stride - channels * sizeof(uint8_t),
.params.q8 = average_pooling_op->q8_avgpool_params,
};
average_pooling_op->compute.type = xnn_parallelization_type_2d;
average_pooling_op->compute.range[0] = batch_size;
average_pooling_op->compute.range[1] = output_height;
if (pooling_size <= mr) {
average_pooling_op->context.average_pooling.unipass_ukernel = xnn_params.q8.avgpool.up;
average_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_average_pooling_unipass;
} else {
average_pooling_op->context.average_pooling.multipass_ukernel = xnn_params.q8.avgpool.mp;
average_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_average_pooling_multipass;
}
average_pooling_op->state = xnn_run_state_ready;
average_pooling_op->last_input = input;
average_pooling_op->last_input_height = input_height;
average_pooling_op->last_input_width = input_width;
average_pooling_op->valid_batch_size = max(valid_batch_size, batch_size);
return xnn_status_success;
}
enum xnn_status xnn_setup_average_pooling2d_nhwc_f32(
xnn_operator_t average_pooling_op,
size_t batch_size,
size_t input_height,
size_t input_width,
const float* input,
float* output,
pthreadpool_t threadpool)
{
if (average_pooling_op->type != xnn_operator_type_average_pooling_f32) {
xnn_log_error("failed to setup Average Pooling (F32) operator: operator type mismatch");
return xnn_status_invalid_parameter;
}
average_pooling_op->state = xnn_run_state_invalid;
if (!xnn_params.initialized) {
xnn_log_error("failed to setup Average Pooling operator: XNNPACK is not initialized");
return xnn_status_uninitialized;
}
if (input_width == 0 || input_height == 0) {
xnn_log_error(
"failed to setup Average Pooling operator with %zux%zu input: input dimensions must be non-zero",
input_width, input_height);
return xnn_status_invalid_parameter;
}
if (batch_size == 0) {
average_pooling_op->state = xnn_run_state_skip;
return xnn_status_success;
}
average_pooling_op->batch_size = batch_size;
average_pooling_op->input_height = input_height;
average_pooling_op->input_width = input_width;
average_pooling_op->input = input;
average_pooling_op->output_height = compute_output_dimension(
average_pooling_op->padding_top + input_height + average_pooling_op->padding_bottom,
average_pooling_op->kernel_height,
average_pooling_op->stride_height);
average_pooling_op->output_width = compute_output_dimension(
average_pooling_op->padding_left + input_width + average_pooling_op->padding_right,
average_pooling_op->kernel_width,
average_pooling_op->stride_width);
average_pooling_op->output = output;
size_t valid_batch_size = 0;
if (input == average_pooling_op->last_input &&
input_height == average_pooling_op->last_input_height &&
input_width == average_pooling_op->last_input_width)
{
valid_batch_size = average_pooling_op->valid_batch_size;
if (batch_size <= valid_batch_size) {
average_pooling_op->compute.range[0] = batch_size;
average_pooling_op->state = xnn_run_state_ready;
return xnn_status_success;
}
}
const size_t pooling_height = average_pooling_op->kernel_height;
const size_t pooling_width = average_pooling_op->kernel_width;
const size_t pooling_size = pooling_height * pooling_width;
const size_t output_height = average_pooling_op->output_height;
const size_t output_width = average_pooling_op->output_width;
// Micro-kernel may read up to (mr - 1) elements after the end of indirection buffer.
const uint32_t mr = xnn_params.f32.avgpool.mr;
assert(mr == xnn_params.f32.pavgpool.mr);
const size_t step_width = min(average_pooling_op->stride_width, pooling_width);
const size_t step_height = pooling_size + (output_width * step_width - 1) * pooling_height;
const size_t indirection_buffer_size = sizeof(void*) * ((mr - 1) + batch_size * output_height * step_height);
const void** indirection_buffer = (const void**) realloc(average_pooling_op->indirection_buffer, indirection_buffer_size);
if (indirection_buffer == NULL) {
xnn_log_error("failed to allocate %zu bytes for indirection buffer", indirection_buffer_size);
return xnn_status_out_of_memory;
}
average_pooling_op->indirection_buffer = indirection_buffer;
xnn_indirection_init_dwconv2d(
average_pooling_op, valid_batch_size, step_height, step_width, 2 /* log2(sizeof(float)) */);
const size_t channels = average_pooling_op->channels;
const size_t indirect_input_height_stride = step_height * sizeof(void*);
const size_t output_width_stride = average_pooling_op->output_pixel_stride * sizeof(float);
const size_t output_height_stride = output_width * output_width_stride;
switch (average_pooling_op->ukernel.type) {
case xnn_ukernel_type_average_pooling:
{
const uint32_t qr = xnn_params.f32.avgpool.qr;
const size_t multipass_adjustment =
pooling_size > mr ? round_up(pooling_size - mr, qr) + mr - qr : 0;
average_pooling_op->context.average_pooling = (struct average_pooling_context) {
.indirect_input = indirection_buffer,
.indirect_input_batch_stride = output_height * indirect_input_height_stride,
.indirect_input_height_stride = indirect_input_height_stride,
.output = output,
.output_batch_stride = output_height * output_height_stride,
.output_height_stride = output_height_stride,
.output_width = output_width,
.pooling_size = pooling_size,
.channels = channels,
.zero = average_pooling_op->zero_buffer,
.input_increment = (pooling_height * step_width - multipass_adjustment) * sizeof(void*),
.output_increment = output_width_stride - channels * sizeof(float),
.params.f32 = average_pooling_op->f32_avgpool_params,
};
if (pooling_size <= mr) {
average_pooling_op->context.average_pooling.unipass_ukernel = xnn_params.f32.avgpool.up;
average_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_average_pooling_unipass;
} else {
average_pooling_op->context.average_pooling.multipass_ukernel = xnn_params.f32.avgpool.mp;
average_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_average_pooling_multipass;
}
break;
}
case xnn_ukernel_type_pixelwise_average_pooling:
{
if (input_height != average_pooling_op->last_input_height ||
input_width != average_pooling_op->last_input_width)
{
const size_t pixelwise_buffer_size = output_height * output_width * sizeof(float);
float* pixelwise_buffer = (float*) realloc(average_pooling_op->pixelwise_buffer, pixelwise_buffer_size);
if (pixelwise_buffer == NULL) {
xnn_log_error("failed to allocate %zu bytes for pixelwise buffer", pixelwise_buffer_size);
return xnn_status_out_of_memory;
}
average_pooling_op->pixelwise_buffer = pixelwise_buffer;
float* pixelwise_pointer = pixelwise_buffer;
for (size_t output_y = 0; output_y < output_height; output_y++) {
const size_t input_y_start = doz(output_y * average_pooling_op->stride_height, average_pooling_op->padding_top);
const size_t input_y_end =
min(doz(output_y * average_pooling_op->stride_height + average_pooling_op->kernel_height, average_pooling_op->padding_top), input_height);
const uint32_t input_y_range = (uint32_t) (input_y_end - input_y_start);
for (size_t output_x = 0; output_x < output_width; output_x++) {
const size_t input_x_start = doz(output_x * average_pooling_op->stride_width, average_pooling_op->padding_left);
const size_t input_x_end =
min(doz(output_x * average_pooling_op->stride_width + average_pooling_op->kernel_width, average_pooling_op->padding_left), input_width);
const uint32_t input_x_range = (uint32_t) (input_x_end - input_x_start);
*pixelwise_pointer++ = 1.0f / ((float) (int32_t) (input_y_range * input_x_range));
}
}
}
const uint32_t qr = xnn_params.f32.pavgpool.qr;
const size_t multipass_adjustment =
pooling_size > mr ? round_up(pooling_size - mr, qr) + mr - qr : 0;
average_pooling_op->context.pixelwise_average_pooling = (struct pixelwise_average_pooling_context) {
.indirect_input = indirection_buffer,
.indirect_input_batch_stride = output_height * indirect_input_height_stride,
.indirect_input_height_stride = indirect_input_height_stride,
.pixelwise_buffer = average_pooling_op->pixelwise_buffer,
.pixelwise_buffer_height_stride = output_width * sizeof(float),
.output = output,
.output_batch_stride = output_height * output_height_stride,
.output_height_stride = output_height_stride,
.output_width = output_width,
.pooling_size = pooling_size,
.channels = channels,
.zero = average_pooling_op->zero_buffer,
.input_increment = (pooling_height * step_width - multipass_adjustment) * sizeof(void*),
.output_increment = output_width_stride - channels * sizeof(float),
.params.f32 = average_pooling_op->f32_output_params,
};
if (pooling_size <= mr) {
average_pooling_op->context.pixelwise_average_pooling.unipass_ukernel = xnn_params.f32.pavgpool.up;
average_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_pixelwise_average_pooling_unipass;
} else {
average_pooling_op->context.pixelwise_average_pooling.multipass_ukernel = xnn_params.f32.pavgpool.mp;
average_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_pixelwise_average_pooling_multipass;
}
break;
}
default:
XNN_UNREACHABLE;
}
average_pooling_op->compute.type = xnn_parallelization_type_2d;
average_pooling_op->compute.range[0] = batch_size;
average_pooling_op->compute.range[1] = output_height;
average_pooling_op->state = xnn_run_state_ready;
average_pooling_op->last_input = input;
average_pooling_op->last_input_height = input_height;
average_pooling_op->last_input_width = input_width;
average_pooling_op->valid_batch_size = max(valid_batch_size, batch_size);
return xnn_status_success;
}